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### 66 Cards in this Set

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 The science that deals with the collection, classification, summarizing, organizing, analyzing and interpreting of numerical information statistics three areas of statistics descriptive, statistical, prediction and regression all of the items of interest in a given problem population finite subset drawn from the population sample characteristic or property of interest variable making a statement about a characteristic of the overall true population based on a characteristic from a random sample drawn from the population statistical inference can be described numerically; age, weight, height, size of family, monthly sales quantitative data categorical data; color of eyes, employment status, defect or no defect qualitative data difference between the estimator and the true population parameter; sampling info vs population info sampling error all other errors that cause a difference btw estimator and population parameter nonsampling error where every subset of fixed size in the population has equal probability of being included random sample a variable that contains the outcomes of a chance experiment random variable r.v. can take finite number of values (countable);think of as “separated values” discrete random variable r.v. can take any value in intervals (measurements);always infinite continuous random variable # of defectives in a lot of size 50, type of customer complaints discrete random variable wait time, response time of a computer system continuous random variable Each probability p(x) must be between 0 and 1 inclusive probability distribution requirements can only take on two values; yes/no, pass/fail, etc.; n identical trials in the experiment; trials are independent binomial random variables; characteristics of... probability of success p probability of failure q relationship between p and q p + q = 1 Relative frequency notion of probability Binomial formula Binomial table Normal approximation to the binomial 4 methods for calculating binomial probabilities It gives us the combination count, or the number of samples that produces exactly x successes in n trials Binomial coefficient 4 things to describe descriptive statistics location, dispersion, shape, data patters measures of central tendency location measures of variability dispersion measures of central tendency mean, median, mode measures of variability standard deviation, variance, range, interquartile range shape skew most frequently occurring observation in a distribution mode middlemost observation in an ordered array median sum of all the values divided by the number of values (sample size or population size) mean [max minus min] range measures of location that divide a group of data into four subgroups; lower quartile, middle quartile, upper quartile; Q(u)-Q(l); Range of the middle 50% of the distribution Interquartile range 25% lower quartile 50% middle quartile 75% upper quartile Tells how far on average each value is away from the mean; notation: population / sample variance notation: population / sample standard deviation Standardized scores; Numerical value reflects the standing of a measurement relative to the mean; Tells how far away from the mean the value is in terms of standard deviations; Algebraic sign (+ or -) indicates whether the measurement is larger or smaller relative to the mean z-score data points that do not follow the general pattern of data outliers extreme values of the observed data whiskers Based on the sample evidence; making a statement about the population statistical inference Based on known populations; making a statement about the probability of an event probability act or process of observation that leads to a single outcome that cannot be predicted with certainty experiment the most basic outcome of an experiment sample point all of the sample points of an experiment sample space specific collection of sample points event probability rules for sample points 1) individual probabilities must lie between 0 and 1 (inclusive) 2) the sum of probabilities of all sample points in a sample space must equal 1 # of sample points that correspond to an event relative to the total # of sample points in the sample space probability the event that A does not occur complement of event A if either A or B or both occurs union if both A and B simultaneously occur intersection if A and B have no sample space outcomes in common {AunionB contains no sample points} mutually exclusive events We have information – prior knowledge – that affects the probability of an event conditional probability if the occurrence of one does not alter the probability of the other independent events may take on any value in an interval continuous rv Make a statement about the overall true population parameters Based on information from a random sample statistical inference Tells how far, on average the sample statistic is away from the population parameter; SE st. dev. of sampling distribution Mean of the sampling distribution of the statistic X-Bar equals the mean of the population; Standard deviation of the sampling distribution of the statistic X-Bar equals the standard deviation of the population divided by the square root of the sample size n central limit theorem Provides a single value Based on observations from 1 sample Gives no information about how close the point estimator is to the unknown population parameter point estimate Provides a range of values Based on observations from 1 sample Gives information about closeness to unknown population parameter Stated in terms of probability To know exact closeness requires knowing population parameter that is usually unknown interval estimate the measure of the precision of the estimate margin for error Involve qualitative variables Fraction or % of population in a category If two qualitative outcomes, binomial distribution Proportion observed level of significance p-value probability of obtaining a test statistic more extreme (or than actual observed value given H0 is true p-value